CN116187844A - Heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test - Google Patents
Heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test Download PDFInfo
- Publication number
- CN116187844A CN116187844A CN202310119965.1A CN202310119965A CN116187844A CN 116187844 A CN116187844 A CN 116187844A CN 202310119965 A CN202310119965 A CN 202310119965A CN 116187844 A CN116187844 A CN 116187844A
- Authority
- CN
- China
- Prior art keywords
- data
- drum
- torque
- vehicle
- reconstruction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005192 partition Methods 0.000 title claims abstract description 63
- 238000012360 testing method Methods 0.000 title claims abstract description 46
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 40
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 39
- 238000012544 monitoring process Methods 0.000 title claims abstract description 28
- 238000011156 evaluation Methods 0.000 title description 4
- 238000000034 method Methods 0.000 claims abstract description 54
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 239000000446 fuel Substances 0.000 claims description 79
- 238000000638 solvent extraction Methods 0.000 claims description 16
- 238000004140 cleaning Methods 0.000 claims description 8
- 239000000498 cooling water Substances 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 125000004122 cyclic group Chemical group 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 2
- 230000037430 deletion Effects 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
- 238000005215 recombination Methods 0.000 description 4
- 230000006798 recombination Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000002283 diesel fuel Substances 0.000 description 1
- 238000010790 dilution Methods 0.000 description 1
- 239000012895 dilution Substances 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a heavy vehicle carbon emission and oil consumption partition reconstruction assessment method suitable for remote monitoring and actual road testing, which is characterized in that remote monitoring data or actual road testing data of a target vehicle type are used as real-time data of the vehicle, the real-time data of the vehicle per second and the engine speed and net output torque of rotary drum data are partitioned and data-associated, the average oil consumption level of the target vehicle type is obtained through partitioned data clustering, the reconstruction data of the carbon emission or oil consumption level of each partition is obtained through data reconstruction and the average oil consumption level of the target vehicle type based on rotary drum data prediction, different partition reconstruction data are spliced according to the engine cycle process of the target vehicle type during oil consumption authentication on a chassis dynamometer, and whether the carbon emission or oil consumption level of the target vehicle type in the use process is in compliance with an authentication result is obtained. The invention improves the use efficiency of remote monitoring and actual road test data, and has simple calculation, high accuracy and strong adaptability.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road testing.
Background
The carbon dioxide emissions in the transportation industry account for about 10% of the total carbon emissions nationwide, with road traffic accounting for about 80% of the carbon emissions in the transportation industry. Carbon emissions from heavy vehicles will be a key area of concern over time. The carbon emission of the heavy-duty car is directly related to the oil consumption. In order to better control the emission of the heavy-duty vehicle, the construction of a remote on-line monitoring system for the propulsion heavy-duty diesel vehicle is imperative. The requirements of remote monitoring and actual road testing are newly increased in the Chinese six-stage heavy vehicle emission standard GB 17691-2018. The remote monitoring system of the heavy-duty car is developed for years, various functions are gradually perfected, and the networking quantity of the vehicles is gradually increased. The actual road test adopts a portable vehicle-mounted emission test system (PEMS) to record actual running data and emission results of the vehicle on-board. According to the GB/T27840-2021 standard, the fuel consumption test of the heavy vehicle is carried out on a chassis dynamometer according to the running working condition of the Chinese vehicle specified by GB/T38146.2-2019, and the full load condition of the vehicle is simulated. In the actual running process of the vehicle, whether through remote monitoring or PEMS test, the standard fuel consumption test condition cannot be reproduced due to the condition differences of load, driver, wind resistance, road condition and the like, so that the fuel consumption (carbon emission) of the vehicle cannot be effectively monitored.
Disclosure of Invention
Aiming at the technical defects existing in the prior art, the invention provides a method for reconstructing and evaluating carbon emission and oil consumption of a heavy truck for remote monitoring and actual road testing, which is used for applying the vehicle operation data obtained in the process of remote monitoring big data or actual road testing to the monitoring of oil consumption (carbon emission) of the heavy truck, thereby realizing the expansion of the monitoring of the oil consumption (carbon emission) of the heavy truck from the current new vehicle authentication stage to the production consistency and the in-use compliance inspection stage, improving the utilization rate of the remote monitoring and test data, improving the monitoring efficiency of the oil consumption (carbon emission) of the heavy truck and realizing the power assisting double-carbon target.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a method for reconstructing and evaluating carbon emission and oil consumption of a heavy truck in a partitioned manner comprises the following steps:
data cleaning is carried out on actual running data of a target vehicle type, wherein the actual running data comprises remote monitoring data or actual road test data, and effective data of the vehicle are obtained; preprocessing vehicle effective data and obtained drum data of the vehicle to obtain net output torque of the vehicle effective data and the drum data per second and power of the drum data per second; the method comprises the steps of carrying out torque partitioning on the net output torque of the vehicle effective data and the drum data every second based on the torque partitioning interval, and carrying out rotating speed partitioning on the engine rotating speed of the vehicle effective data and the drum data every second based on the rotating speed partitioning interval, so that the data are uniformly numbered, and the vehicle effective data and the drum data establish corresponding connection after the torque partitioning and the rotating speed partitioning; processing fuel flow data corresponding to the effective data per second through data clustering to obtain a fuel flow average value; based on the fuel flow average value, processing the drum data of every second through cyclic reconstruction, predicting, obtaining fuel flow reconstruction data of every second and calculating CO of every second 2 Discharge flow reconstruction data; according to the engine cycle process of the target vehicle model during fuel consumption authentication on the chassis dynamometer, the fuel flow\CO of different torque\rotating speed partition calculation is carried out 2 And (3) splicing the results of the emission flow reconstruction data values, and calculating to obtain the carbon emission and oil consumption level of the target vehicle model in the using process, wherein the compliance is compared with the authentication result to determine whether the compliance is satisfactory.
The actual operation data comprise cooling water temperature, vehicle speed, engine rotating speed, reference torque, actual torque and fuel flow.
Wherein the drum data includes vehicle speed, engine speed, reference torque, actual torque, friction torque, fuel flow and CO 2 The emission flow is obtained by selecting a chassis dynamometer to test the drum circulation and measuring the fuel consumption of the hot vehicle according to the GB/T27840 standard; the vehicle providing the drum data is the same as the vehicle engine model and ECU calibration data providing the actual road test data.
And performing data cleaning on the actual operation data, wherein the data comprises the step of removing the data that the temperature of cooling water of an engine is less than 70 ℃ and the rotating speed of the engine is less than 300r/min.
Wherein, the total fuel flow rate in L/h in the effective data of the torque subareas with the numbers of a and b and the rotating speed subareas is recorded as a set Q a,b If set Q a,b The number of elements in the set is less than or equal to 5, and the set Q is discarded a,b The method comprises the steps of carrying out a first treatment on the surface of the For a set Q with the number of elements being more than 5 a,b Averaging data sets
And standard deviation sigma a,b Deletion of less than
And greater than->
Calculating the average value mu of the remaining elements a,b An average value calculated for each torque/rotation speed partition is obtained, thereby obtaining a fuel flow average value.
Wherein, the net output torque of the vehicle effective data and the drum data is calculated by the following formula;
wherein T is net,i For net output torque of ith second, T ref Is the reference torque; t (T) act,i And T fri,i Actual torque and friction torque for the i second respectively;
calculating the power of drum data per second according to the following formula;
wherein P is i Power for the i second; n is n i The i-th second engine speed.
Wherein, calculate the torque partition interval according to the following formula;
T bin ＝T ref ÷20，T bin for torque zone spacing, T ref Is the reference torque;
calculating a rotation speed partition interval according to the following formula;
n bin ＝n max ÷25，n bin for the interval of the rotating speed partition, n max Is the maximum value of the engine speed;
the vehicle effective data and the drum data are numbered a according to the following formula;
a＝floor(T net,i ÷T bin )，T net,i net output torque for the ith second;
the vehicle effective data and the drum data are numbered b according to the following formula;
b＝floor(n i ÷n bin )，n i the i-th second engine speed.
Wherein, when processing the second-by-second drum data by cyclic reconstruction based on the fuel flow average value, if the torque/rotation speed partition of the drum data of a certain second has a corresponding mu a,b Predicted fuel flow per second
If the torque/rotation speed partition of the drum data of a certain second does not have the corresponding mu a,v Predicting according to an unknown partition fuel flow result prediction method, and recording second-by-second prediction data as reconstruction data, wherein the duration is the same as that of the drum data;
the CO per second was calculated according to the carbon balance principle according to the following formula 2 Discharge flow reconstruction data;
in the method, in the process of the invention,
reconstruction of data of CO for ith second 2 Discharge flow rate ρ d Is diesel density;
unknown partition fuelFlow result prediction: if the number a in the data of the torque/rotating speed partition of the drum data is no corresponding mu in the partition b a,b Predicting by a one-dimensional linear interpolation method or a two-dimensional fitting method; the order of preference is two-dimensional fitting>Rotational speed zoned one-dimensional linear interpolation>Torque partitioning one-dimensional linear interpolation; if the predicted value is less than 0, the predicted value is considered to be 0.
The method comprises the steps of calculating to obtain carbon emission and fuel consumption level of a target vehicle model in the using process, and respectively calculating fuel flow and CO of drum data and reconstruction data when the compliance is in compliance with the authentication result 2 Specific discharge, calculating relative errors, and judging fuel flow and CO 2 Whether the relative error limit of the specific emissions is within a set threshold value:
CO of drum data 2 The calculation formula of the specific emission is as follows:
CO reconstructing data 2 The specific emission level is calculated as follows:
if the starting time of a certain part of the drum test cycle is the p-th second, the ending time is the q-th second, calculating the fuel consumption of the reconstruction data according to the following formula;
in the formula, v i Vehicle speed of the ith second, P i For the power of the i-th second,
CO for the ith second of drum data 2 The amount of the discharged water is controlled,
reconstruction of data of CO for ith second 2 The discharge quantity, i and J are natural numbers, i is more than or equal to 1 and less than or equal to J and J is the data duration of the rotary drum, and the instantaneous CO of CVS equipment is set 2 The data delay time of the test result relative to the instantaneous fuel flow of the vehicle data stream is Δt seconds, and Δt=1s to Δt=20s are calculated respectively +.>
And->
Pearson correlation coefficient->
Increase in 1 second step->
Δt corresponding to the maximum value is denoted as Δt max ；
The relative error is calculated using the formula:
wherein E is r As relative error, M cal Calculation result of reconstruction data, m exp Is the calculation result of the drum data.
According to the invention, the fuel consumption authentication cycle of the target vehicle type on the chassis dynamometer is reproduced by utilizing the vehicle remote networking monitoring data or the actual road testing data through a partition recombination method, so that the influences of factors such as load, drivers, wind resistance, road conditions and the like are eliminated, the use efficiency of the remote monitoring and the actual road testing data is improved, the calculation method is simple and feasible, the accuracy is high, the adaptability is strong, the application range is wide, and the problem that the carbon emission and the fuel consumption of the heavy vehicle cannot be monitored in the prior art can be solved.
Drawings
FIG. 1 is a flow chart of a heavy truck carbon emission and fuel consumption partition recombination assessment method of the invention.
FIG. 2 is a schematic representation of the average calculated for each partition of the present invention.
Fig. 3 is a schematic diagram of the Pearson (Pearson) correlation coefficient calculation result of the present invention.
Fig. 4 is a schematic diagram of a comparison of drum data and predicted data (reconstruction data) of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention relates to a method for carrying out regional recombination assessment on carbon emission and fuel consumption of a heavy vehicle, which is suitable for remote monitoring and actual road testing, wherein remote emission monitoring data or actual road testing results of a target vehicle type are used as calculation input, real-time data and drum data of the vehicle per second are partitioned according to the rotation speed and torque of an engine, the fuel consumption level of each partition of the real-time data of the vehicle is averaged to obtain real-time fuel consumption of the vehicle, the data of each partition of the real-time data of the vehicle is correspondingly associated with the drum data per second, the data of the drum data is reconstructed based on the real-time fuel consumption of the vehicle, and the fuel flow and CO of the vehicle are obtained by prediction 2 Reconstruction data of the discharge flow rate; according to the engine cycle process when the fuel consumption of the target vehicle type on the chassis dynamometer is authenticated, the reconstruction data, namely the prediction data, of different partitions are continuously arranged, namely spliced, one second by one second, so that the carbon emission or fuel consumption level of the vehicle type in the use process is calculated and obtained, and whether the compliance of the vehicle type is compliant is checked by comparing the carbon emission or fuel consumption level with the authentication result of the chassis dynamometer of the vehicle fuel consumption (for example, according to GB/T27840 standard) and the emission (for example, according to GB17691 standard).
As shown in fig. 1, the method for restructuring and evaluating carbon emission and oil consumption of a heavy truck suitable for remote monitoring and actual road testing in a partitioned manner according to the embodiment of the invention comprises the following implementation steps:
step 1) data input
1.1 actual operating data:
the actual road test data or emission remote monitoring data of the vehicle are selected as actual operation data, and the data items are shown in the following table 1, and the data frequency is 1Hz. The actual operating data should contain as rich vehicle driving conditions as possible, covering as wide an engine load range as possible. For actual road test, the input data is an OBD data stream recorded by the OBD communication equipment, and the vehicle load proposal is not lower than 30%. For emission remote monitoring data, the input data is a data stream information body. The actual running data length should meet the data cleaning requirements in step 2.
| Data item | Actual road test | Emission remote monitoring |
| Cooling water temperature (DEG C) | √ | √ |
| Vehicle speed (km/h) | √ | √ |
| Engine speed (r/min) | √ | √ |
| Reference torque (Nm) | √ | √ |
| Actual torque (%) | √ | √ |
| Friction torque (%) | √ | √ |
| Fuel flow (L/h) | √ | √ |
TABLE 1
In the embodiment of the invention, the actual road test data of the vehicle is selected, the data source is the actual road test result of a certain heavy vehicle, the load is 39.1%, and the test duration is 53230 seconds, as shown in table 2.
TABLE 2
1.2 vehicle fuel consumption amount measurement data (hereinafter referred to as "drum data"):
a chassis dynamometer test cycle (hereinafter referred to as a "drum cycle") was selected, thermal vehicle fuel consumption measurements were made according to the GB/T27840 standard, drum data shown in table 2 was recorded as input, the data frequency was 1Hz, and the data length was the same as the drum cycle duration (cycle duration noted J).
The vehicle providing the drum data is the same as the vehicle engine model providing the actual road test data and the ECU calibration data.
| Data item | Data source |
| Vehicle speed (km/h) | Engine data flow |
| Engine speed (r/min) | Engine data flow |
| Reference torque (Nm) | Engine data flow |
| Actual torque (%) | Engine data flow |
| Friction torque (%) | Engine data flow |
| Part 1 (if any) or urban area cycle fuel consumption (L/100 km) | Bag picking |
| 2 (if any) or highway cycle fuel consumption (L/100 km) | Bag picking |
| 3 (if any) or high-speed cycle fuel consumption (L/100 km) | Bag picking |
TABLE 3 Table 3
In the embodiment of the invention, the vehicle is suitable for C-WTYC test cycle, 1800s is taken as a total, 100% load is simulated through resistance setting, and the test results are shown in the following table 4:
| Time | vehicle speed | Engine speed | Reference torque | Actual torque | Friction torque | Fuel flow rate | CO 2 Discharge flow rate |
| s | km/h | r/min | Nm | ％ | ％ | L/h | g/s |
| 1 | 8.3 | 732.5 | 315 | 20.7 | 7 | 1.4 | 8.3 |
| 2 | 8.3 | 859.1 | 315 | 26.9 | 7 | 0.5 | 8.3 |
| 3 | 10.1 | 1028.6 | 315 | 49.4 | 7.7 | 3.6 | 10.1 |
| 4 | 11.7 | 1097.0 | 315 | 16.6 | 8 | 1.5 | 11.7 |
| 5 | 10.6 | 1016.6 | 315 | 0.3 | 8.0 | 0 | 10.7 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1800 | 0 | 749.7 | 315 | 7 | 7 | 0.4 | 0 |
TABLE 4 Table 4
Step 2) data preprocessing
2.1 data cleaning:
and eliminating the data meeting the following conditions from the actual operation data.
(1) The temperature of engine cooling water is less than 70 ℃;
(2) The engine speed is less than 300r/min.
The data subjected to data cleaning is marked as "valid data"; and (3) the effective data duration is longer than 2h, and if the effective data duration does not meet the requirement, returning to the step (1) to adjust the input data.
In the embodiment of the invention, the effective data in the actual operation data remains 51673s after data cleaning.
2.2 data preprocessing:
2.2.1 the net output torque of the effective data and drum data is calculated in seconds by seconds as follows.
Wherein T is net,i Net output torque in Nm for the ith second; t (T) ref A fixed value for reference torque, unit Nm; t (T) act,i And T fri,i The actual torque and friction torque in% for the ith second, respectively.
2.2.2 net output torque T for the ith second based on drum data net,i The power of the drum data per second is calculated as follows.
Wherein P is i Power in kW for the i second; n is n i The unit is r/min for the i second engine speed.
In the embodiment of the present invention, the preprocessing result of the actual operation data is shown in the following table 5:
| Time | net output torque |
| s | Nm |
| 1 | 217.3 |
| 2 | 119.7 |
| 3 | 132.3 |
| 4 | 81.9 |
| 5 | 34.6 |
| ... | ... |
| 51673 | 84 |
TABLE 5
Step 3) data partitioning
3.1 torque partitioning:
based on parameter torque, rational selection of torque partition interval T according to engine torque range bin The recommended range is 20-200 Nm, and the torque partition interval T can be calculated according to the following formula bin 。
T bin ＝T ref ÷20，T ref Is the reference torque;
based on torque zone spacing T bin Net output torque T for ith second of vehicle payload data net,i The second-by-second effective data, numbered a, floor, representing a rounding down operation, is obtained according to the following formula.
a＝floor(T net,i ÷T bin )，T net,i Net output torque for the ith second;
3.2 speed partitioning:
3.2.1 rational selection of the rotational speed partition interval n bin The recommended range is 50-200 r/min, and the rotating speed partition interval n is calculated according to the following formula bin 。
n bin ＝n max ÷25，n max The unit r/min is the maximum engine speed.
3.2.2 based on the rotational speed partition interval n bin Engine speed n for the ith second of vehicle payload data i The second-by-second effective data, numbered b, is obtained as follows.
b＝floor(n i ÷n bin )，
The embodiment of the invention selects T bin ＝25Nm，n bin Effective data partition is shown in table 6 =100 r/min:
| Time | a | b |
| s | - | - |
| 1 | 16 | 8 |
| 2 | 16 | 4 |
| 3 | 16 | 5 |
| 4 | 16 | 3 |
| 5 | 16 | 1 |
| ... | ... | ... |
| 51673 | 7 | 0 |
TABLE 6
Step 4) data clustering
All fuel flows (L/h) numbered a, b in the vehicle effective data are recorded as a set Q a,b . If set Q a,b If the number of elements in the set is less than or equal to 5, discarding the set Q a,b Subsequent calculations are not included.
Set Q with number of all elements greater than 5 a,b Averaging data sets
And standard deviation sigma a,b . Assuming that the data distribution is compliant with normal distribution, deleting the set Q according to the 3 sigma principle of normal distribution a,b Less than->
And greater than->
Calculating the average value mu of the remaining elements a,b 。
In the embodiment of the invention, after the effective data of the vehicle is clustered and the discrete data except 3 sigma are deleted, the average value of the fuel flow calculated by each region is shown as a figure 2, each small square in the figure represents 1 partition, and each different small square corresponding to different a and b numbers.
Step 5) cyclic reconstruction
5.1 cycle reconstruction:
the method of step 3) is to divide the torque/rotation speed of the drum data into a, b numbers corresponding to the data of the torque/rotation speed of the drum data every second, and obtain the predicted fuel flow of the second
And (3) carrying out a and b numbering on the data uniformly to realize the association of the drum data and the real-time operation data. If the number of the rotating drums is a certain secondAccording to the partition there is no corresponding mu a,b Predicting the outcome of the region in section 5.2; recording the second-by-second fuel flow prediction data as reconstruction data, wherein the duration is the same as that of the drum data;
and calculating the CO per second according to the carbon balance principle according to the following formula 2 Emission flow reconstruction data.
In the method, in the process of the invention,
reconstruction of data of CO for ith second 2 Discharge flow, in g/s, drum data for this second are labeled a, b; ρ d The density of the diesel oil is expressed in g/L.
5.2 unknown partition Fuel flow outcome prediction:
in the embodiment of the invention, if the number a in the drum data is the number b, the partition b does not have the corresponding mu a,b Predicting by a one-dimensional linear interpolation method or a two-dimensional fitting method; the priority order is: two-dimensional fitting>Rotational speed zoned one-dimensional linear interpolation>Torque zoning one-dimensional linear interpolation. If the predicted value is less than 0, the predicted value is considered to be 0. The two-dimensional interpolation is performed as follows:
and fitting a two-dimensional plane equation by taking a and b as coordinates through all known partition data, and solving partition results with the numbers of a and b.
The processing method of the one-dimensional linear interpolation of the rotating speed partition comprises the following steps:
if the number of the effective data partitions of the torque partition a is greater than 3, predicting the partition result of the rotating speed partition b by a one-dimensional linear interpolation method, wherein an example is shown in the following table 7.
TABLE 7
The processing method of the torque partition one-dimensional linear interpolation comprises the following steps:
if the number of the effective data partitions with the rotation speed of b is greater than 3, predicting the partition result with the torque of a by a one-dimensional linear interpolation method, wherein an example is shown in the following table 8.
TABLE 8
The reconstruction data corresponding to the drum data in the embodiment of the invention is:
TABLE 9
Step 6) Fuel consumption and CO 2 Prediction
6.1CO 2 Alignment of data:
instantaneous CO due to CVS device (engine-out full-flow dilution constant volume sampling system) 2 The test results have a delay relative to the instantaneous fuel flow of the vehicle data stream, so that it is necessary to consider the CO in the reconstructed data from the start to the end 2 Discharge flow rate
(in subscripts ":" before and after numerals indicate start and end times) and CO in drum data 2 Discharge flow rate
Data alignment is performed.
Let the data delay time be Δt seconds, calculate Δt=1 to Δt=20, respectively
And->
Pearson correlation coefficient of +.>
Gradually increasing in 1 second steps. Find->
Δt corresponding to the maximum value is denoted as Δt max Deltat max -1 is the delay time, then the data and drum data CO are reconstructed 2 The correspondence after the discharge flow alignment is:
table 10
In the embodiment of the invention
The calculation result of (2) is shown in FIG. 3 +.>
Take Δt at maximum max ＝10s：
Fuel flow and CO for the tumble data and the reconstruction data were calculated at 6.2 and 6.3, respectively 2 The cumulative value of the discharge amount, and the relative error is calculated according to the following formula.
Wherein E is r As relative error, M cal Calculation result of reconstruction data, m exp Is the calculation result of the drum data.
6.2 CO 2 And (3) specific emission:
calculating CO of drum data and reconstruction data 2 Specific emission, and calculating relative error, and then judging CO 2 Whether the relative error limit of the specific emissions is within a set threshold range. Wherein, the relative error limit value of the two can be formulated according to the supervision requirement.
CO of drum data 2 The calculation formula of the specific emission is as follows:
CO reconstructing data 2 The specific emission level is calculated as follows:
in the method, in the process of the invention,
CO for the ith second of drum data 2 Discharge amount (I)>
Reconstruction of data of CO for ith second 2 The discharge quantity, i and J are natural numbers, i is more than or equal to 1 and J is more than or equal to J.
In the embodiment of the invention, the CO of the drum data and the reconstruction data 2 The specific emissions and relative errors are shown in the following table:
TABLE 11
7.2 fuel consumption:
according to the GB/T27840 standard, fuel consumption is measured by weighting and calculating fuel consumption of 3 parts (only 2 parts for partial CHTC circulation) in a drum test cycle (for C-WTYC circulation, urban circulation, highway circulation and high-speed circulation), wherein the unit is L/100km.
If the starting time of a certain part of the drum test cycle is the p-th second, the ending time is the q-th second, calculating the fuel consumption of the reconstruction data according to the following formula; for each part, the relative error limit may be formulated according to regulatory requirements.
In the formula, v i The i-th second vehicle speed.
In the embodiment of the invention, the calculation results and relative errors of the drum data and the reconstruction data of the fuel consumption of three parts of urban area, highway and high speed in the C-WTYC cycle are shown in the following table:
table 12
The embodiment of the invention adopts the CO of the vehicle model after the data reconstruction 2 The relative error between the specific emission and the fuel consumption and the actual measurement data of the rotary drum is within +/-5 percent, and the method has higher compliance.
Therefore, the partition recombination method provided by the invention is suitable for the test and evaluation of carbon emission and oil consumption of the heavy vehicle for emission remote monitoring and actual road testing.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof;
the present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (9)
1. The method for evaluating the carbon emission and oil consumption partition reconstruction of the heavy truck is characterized by comprising the following steps of:
data cleaning is carried out on actual running data of a target vehicle type, wherein the actual running data comprises remote monitoring data or actual road test data, and effective data of the vehicle are obtained; preprocessing vehicle effective data and obtained drum data of the vehicle to obtain net output torque of the vehicle effective data and the drum data per second and power of the drum data per second; the method comprises the steps of carrying out torque partitioning on the net output torque of the vehicle effective data and the drum data every second based on the torque partitioning interval, and carrying out rotating speed partitioning on the engine rotating speed of the vehicle effective data and the drum data every second based on the rotating speed partitioning interval, so that the data are uniformly numbered, and the vehicle effective data and the drum data are correspondingly linked after the torque partitioning and the rotating speed partitioning; processing fuel flow data corresponding to the effective data per second through data clustering to obtain a fuel flow average value; based on the fuel flow average value, processing the drum data of every second through cyclic reconstruction, predicting and obtaining fuel flow reconstruction data of every second, and calculating CO of every second 2 Discharge flow reconstruction data; according to the engine cycle process of the target vehicle model during fuel consumption authentication on the chassis dynamometer, the fuel flow\CO of different torque\rotating speed partition calculation is carried out 2 And (3) splicing the results of the emission flow reconstruction data values, and calculating to obtain the carbon emission and oil consumption level of the target vehicle model in the using process, wherein the compliance is compared with the authentication result to determine whether the compliance is satisfactory.
2. The method for evaluating carbon emission and fuel consumption partition reconstruction of a heavy vehicle according to claim 1, wherein the actual operation data includes a cooling water temperature, a vehicle speed, an engine speed, a reference torque, an actual torque, and a fuel flow.
3. The method for reconstructing and evaluating carbon emission and oil consumption of a heavy vehicle according to claim 1, wherein the drum data comprises vehicle speed, engine speed, reference torque, actual torque, friction torque and fuel flow, and is obtained by measuring fuel consumption of a hot vehicle according to GB/T27840 standard by selecting a chassis dynamometer to test drum circulation; the vehicle providing the drum data is the same as the vehicle engine model and ECU calibration data providing the actual road test data.
4. The method for evaluating carbon emission and oil consumption partition reconstruction of a heavy vehicle according to claim 1, wherein the data cleaning of the actual operation data comprises removing data of engine cooling water temperature less than 70 ℃ and engine rotation speed less than 300r/min.
5. The method for evaluating carbon emission and fuel consumption partition reconstruction of a heavy vehicle according to claim 1, wherein the net output torque of the vehicle effective data and the drum data per second is calculated according to the following formula;
wherein T is net，i For net output torque of ith second, T ref Is the reference torque; t (T) act，i And T fri，i Actual torque and friction torque for the i second respectively;
calculating the power of drum data per second according to the following formula;
wherein P is i Power for the i second; n is n i The i-th second engine speed.
6. The method for evaluating carbon emission and fuel consumption partition reconstruction of a heavy vehicle according to claim 1, wherein torque partition intervals are calculated according to the following formula;
T bin ＝T ref ÷20，T bin for torque zone spacing, T ref Is the reference torque;
calculating a rotation speed partition interval according to the following formula;
n bin ＝n max ÷25，n bin for the interval of the rotating speed partition, n max Is the maximum value of the engine speed;
the vehicle effective data and the drum data are numbered a according to the following formula;
a＝floor(T net，i ÷T bin )，T net，i net output torque for the ith second;
the vehicle effective data and the drum data are numbered b according to the following formula;
b＝floor(n i ÷n bin )，n i the i-th second engine speed.
7. The method for reconstructing and evaluating carbon emission and fuel consumption of heavy truck according to claim 6, wherein all fuel flows in L/h in the effective data of torque zone and rotation speed zone with numbers of a and b are recorded as set Q a，b If set Q a，b The number of elements in the set is less than or equal to 5, and the set Q is discarded a，b The method comprises the steps of carrying out a first treatment on the surface of the For a set Q with the number of elements being more than 5 a，b Averaging data sets
And standard deviation sigma a，b Deletion of less than ++according to the principle of normal distribution of 3σ>
And is greater than
Calculating the average value mu of the remaining elements a，b An average value calculated for each torque/rotation speed partition is obtained, thereby obtaining a fuel flow average value.
8. The method for estimating carbon emissions and fuel consumption partition reconstruction of a heavy vehicle according to claim 7, wherein the average fuel flow rate is calculated as a cycle reconstruction for every secondIf the torque/rotation speed partition of the drum data of a certain second has a corresponding mu during the drum data processing a，b Predicted fuel flow per second
If the torque/rotation speed partition of the drum data of a certain second does not have the corresponding mu a，b Predicting according to an unknown partition fuel flow result prediction method, and recording second-by-second prediction data as reconstruction data, wherein the duration is the same as that of the drum data;
the CO per second was calculated according to the carbon balance principle according to the following formula 2 Discharge flow reconstruction data;
in the method, in the process of the invention,
reconstruction of data of CO for ith second 2 Discharge flow rate ρ d Is diesel density;
predicting the result of unknown partition fuel flow: if the number a in the data of the torque/rotating speed partition of the drum data is no corresponding mu in the partition b a，b Predicting by a one-dimensional linear interpolation method or a two-dimensional fitting method; the priority order is two-dimensional fitting > rotational speed partition one-dimensional linear interpolation > torque partition one-dimensional linear interpolation; if the predicted value is less than 0, the predicted value is considered to be 0.
9. The method for partitioned reconstruction assessment of carbon emission and oil consumption of heavy vehicle according to claim 1, wherein the carbon emission and oil consumption level of the target vehicle model in the use process is calculated and obtained, and when the compliance of comparison with the authentication result is in compliance, the fuel flow and the CO of the drum data and the reconstruction data are calculated respectively 2 Specific discharge, calculating relative errors, and judging fuel flow and CO 2 Whether the relative error limit of the specific emissions is within a set threshold value:
CO of drum data 2 The calculation formula of the specific emission is as follows:
CO reconstructing data 2 The specific emission level is calculated as follows:
if the starting time of a certain part of the drum test cycle is the p-th second, the ending time is the q-th second, calculating the fuel consumption of the reconstruction data according to the following formula, wherein the test data adopts a drum bag collecting result;
in the formula, v i Vehicle speed of the ith second, P i For the power of the i-th second,
CO for the ith second of drum data 2 The amount of the discharged water is controlled,
reconstruction of data of CO for ith second 2 The discharge quantity, i and J are natural numbers, i is more than or equal to 1 and less than or equal to J and J is the data duration of the rotary drum, and the instantaneous CO of CVS equipment is set 2 The data delay time of the test result relative to the instantaneous fuel flow of the vehicle data stream is Δt seconds, and Δt=1s to Δt=20s are calculated respectively +.>
And->
Pearson correlation coefficient->
Increase in 1 second step->
Δt corresponding to the maximum value is denoted as Δt max ；
The relative error is calculated using the formula:
wherein E is r As relative error, M cal Calculation result of reconstruction data, m exp Is the calculation result of the drum data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310119965.1A CN116187844A (en) | 2023-02-15 | 2023-02-15 | Heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310119965.1A CN116187844A (en) | 2023-02-15 | 2023-02-15 | Heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116187844A true CN116187844A (en) | 2023-05-30 |
Family
ID=86432282
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310119965.1A Pending CN116187844A (en) | 2023-02-15 | 2023-02-15 | Heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116187844A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703185A (en) * | 2023-08-09 | 2023-09-05 | 杭州泽天春来科技有限公司 | Carbon emission analysis device and method for traffic carrier |
| CN118096007A (en) * | 2024-04-26 | 2024-05-28 | 深圳聚瑞云控科技有限公司 | Real-time monitoring system based on wisdom commodity circulation |
-
2023
- 2023-02-15 CN CN202310119965.1A patent/CN116187844A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703185A (en) * | 2023-08-09 | 2023-09-05 | 杭州泽天春来科技有限公司 | Carbon emission analysis device and method for traffic carrier |
| CN118096007A (en) * | 2024-04-26 | 2024-05-28 | 深圳聚瑞云控科技有限公司 | Real-time monitoring system based on wisdom commodity circulation |
| CN118096007B (en) * | 2024-04-26 | 2024-07-23 | 深圳聚瑞云控科技有限公司 | Real-time monitoring system based on wisdom commodity circulation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116187844A (en) | Heavy truck carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test | |
| CN110610260B (en) | Driving energy consumption prediction system, method, storage medium and equipment | |
| CN111452619B (en) | Online energy consumption prediction method and system for electric vehicle | |
| CN104034466B (en) | A kind of vehicle power measuring and calculation method, device | |
| CN108681788B (en) | Urban discrete traffic network design method based on active safety | |
| CN108556682B (en) | Driving range prediction method, device and equipment | |
| CN111222199B (en) | Key index selection and equivalent calculation method during association of user and test field | |
| CN114954494B (en) | Heavy commercial vehicle load rapid estimation method | |
| CN112964476B (en) | On-line prediction method for actual road particulate matter emission of heavy-duty diesel vehicle | |
| CN111999188B (en) | Asphalt mortar multiple stress creep testing method based on different driving behavior combinations of road intersection | |
| CN113918890B (en) | Low-load working condition construction method based on mobile window library | |
| CN106326581A (en) | Determination method and device for driving range and automobile | |
| CN110243384A (en) | Determination method, apparatus, equipment and the medium of actual travel emission testing route | |
| CN116702096B (en) | Method and device for measuring and calculating road sliding resistance of vehicle plateau environment | |
| CN115791212B (en) | Method and device for detecting exhaust emission of general vehicle | |
| Gismero et al. | Electric vehicle battery state of health estimation using Incremental Capacity Analysis | |
| Mera et al. | Including engine data for energy and pollutants assessment into the vehicle specific power methodology | |
| Zhang et al. | A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response | |
| Na et al. | Quantifying fuel-saving benefit of low-rolling-resistance tyres from heavy goods vehicle in-service operations | |
| CN104865346A (en) | Method and apparatus for verification of data acquired by vehicle tail gas detection system | |
| CN111896264A (en) | Method and device for generating test working condition of range extender engine and electronic equipment | |
| CN116756986A (en) | Driving motor steady-state reliability test working condition construction method and system | |
| CN116611864A (en) | Urban rail transit train speed and passenger flow prediction method based on RNN | |
| CN106795798A (en) | The aging estimating and measuring method of the catalyst converter of motor vehicles | |
| CN115266135A (en) | Oil consumption quantitative analysis method based on user working condition test data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |